@inproceedings{0475f7847e7844a7a8d07a9257e74a5d,
title = "SECF: Improving SPARQL Querying performance with proactive fetching and Caching",
abstract = "Querying on SPARQL endpoints may be unsatisfactory due to high latency of connections to the endpoints. Caching is an important way to accelerate the query response speed. In this paper, we propose SPARQL Endpoint Caching Framework (SECF), a client-side caching framework for this purpose. In particular, we prefetch and cache the results of similar queries to recently cached query aiming to improve the overall querying performance. The similarity between queries are calculated via an improved Graph Edit Distance (GED) function. We also adapt a smoothing method to implement the cache replacement. The empirical evaluations on real world queries show that our approach has great potential to enhance the cache hit rate and accelerate the querying speed on SPARQL endpoints.",
keywords = "Caching, Mses, Query suggestion, SPARQL",
author = "Zhang, {Wei Emma} and Sheng, {Quan Z.} and Yongrui Qin and Lina Yao and Ali Shemshadi and Kerry Taylor",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 31st Annual ACM Symposium on Applied Computing, SAC 2016 ; Conference date: 04-04-2016 Through 08-04-2016",
year = "2016",
month = apr,
day = "4",
doi = "10.1145/2851613.2851846",
language = "English",
series = "Proceedings of the ACM Symposium on Applied Computing",
publisher = "Association for Computing Machinery (ACM)",
pages = "362--367",
booktitle = "2016 Symposium on Applied Computing, SAC 2016",
address = "United States",
}